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English(EN) Couldn't reproduce a published paper's numbers with its own code and settings. How do I present this? [R]

ML研究人员难以重现已发表论文的结果

作者 PulseAugur 编辑部 · [1 个来源] ·

一位机器学习研究人员在尝试使用作者自己的代码和超参数重现已发表论文的结果时遇到了困境，在两个基准测试上得出的数字显著偏低。该研究人员计划同时展示原始发表的图表和他们重现的结果，但担心审稿人可能因为倾向于信任已发表的数字而驳回他们的发现。他们正在寻求关于如何令人信服地呈现这种差异的建议，是否应该联系原作者，以及如何措辞以避免显得指责，同时仍能突出他们的发现。 AI

影响 强调了机器学习研究中科学可复现性面临的挑战。

排序理由 该集群讨论了一位研究人员难以重现已发表论文结果的问题，这是研究界普遍存在的问题。[lever_c_demoted from research: ic=1 ai=1.0]

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ML研究人员难以重现已发表论文的结果

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该集群讨论了一位研究人员难以重现已发表论文结果的问题，这是研究界普遍存在的问题。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
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Same-day
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完整方法见我们的编辑标准。

报道来源 [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/InsectActive95 ·

    Couldn't reproduce a published paper's numbers with its own code and settings. How do I present this? [R]

    <!-- SC_OFF --><div class="md"><p>I'm writing a machine learning paper. One of my baselines is a published method. I reran it with the authors' released code and the exact hyperparameters listed in their paper. On most benchmarks my numbers match theirs within noise, but on two b…

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